On generating a super-resolution image using a set of images, each image belonging to the set of images is warped in order to align and zoom out to fit it to a grid corresponding to the super-resolution image.
Alignment and zooming are carried out according to a distortion model and consist in associating each pixel location of each image of the set of images with a location within the grid corresponding to the super-resolution image.
Since the pixel resolution/pixel density of the super-resolution image is higher than the pixel resolution/pixel density of the images of the set of images, interpolation is used in order to obtain the super-resolution image.
The interpolation has a visible impact on a warped image. In particular, when warping the image in order to obtain the super-resolution image, high frequencies are reduced, which means that high frequencies are low pass filtered.
Thus, the warped image is smoothed and/or a ringing effect is produced in the warped image.
A solution is proposed in the article “Super-resolution imaging: a survey of current techniques” G. Cristobal, E. Gila, F. Sroubek, J. Flusser, C. Miravet, F. B. Rodriguez. Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII; edited by Luk, Franklin T. Proceedings of the SPIE, Volume 7074, pp. 70740C-1 70740C 18 (2008).
This article deals with the problem as re-sampling with irregular pixels. The weight of each pixel depends on a neural network which is being trained by several images.
However, the proposed solution uses a neural network to compute special weight for image warping, which generates a high computational cost.
The present invention is directed to mitigating the aforesaid limitations and to providing a method for generating a super-resolution image using a set of images, and a device associated with that method, making it possible to obtain a super-resolution image of higher quality.